import torch
from torch.nn import functional as F

input_ = torch.tensor([[1, 2, 0, 3, 1],
                       [0, 1, 2, 3, 1],
                       [1, 2, 1, 0, 0],
                       [5, 2, 3, 1, 1],
                       [2, 1, 0, 1, 1]])

kernel = torch.tensor([[1, 2, 1],
                       [0, 1, 0],
                       [2, 1, 0]])

print(input_.shape)
print(kernel.shape)

input_ = torch.reshape(input_, (1, 1, 5, 5))
kernel = torch.reshape(kernel, (1, 1, 3, 3))

print(input_.shape)
print(kernel.shape)

output = F.conv2d(input_, kernel, stride=1, padding=0)
print(output)

output2 = F.conv2d(input_, kernel, stride=2, padding=0)
print(output2)

# padding填充的内容默认为0（默认padding_mode='zero'）
output3 = F.conv2d(input_, kernel, stride=1, padding=1)
print(output3)
